The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in Malibu, Jousha Tree, Brighton (UK) in 2023. The data is owned by Airbtics.

Airbtics is a short-term rental data & analytics company monitoring 20 million listings from various short-term rental booking sites.

Content

This data file includes all the needed information to find out the exact performance of Airbnb listings. You can find out how many nights were booked in a specific month, and average daily rate.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website. The data was processed by Airbtics.

Inspiration

What is the occupancy rate of listing X in January 2023? What is the average daily rate of a listing Y in January 2023? How many bookings did a listing Z receive in January 2023?

To find more granular data in other cities, visit Airbtics.com

These are the Airbnb statistics on gross revenue by country.

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2024, the North America region had the largest share of Airbnb's gross booking value, with **** billion U.S. dollars.

Description

This dataset provides extensive information about Airbnb properties listed in Los Angeles, California. It offers a wealth of details suitable for analyzing short-term rental trends, exploring traveler behavior, and studying pricing dynamics within one of the most vibrant tourism markets in the U.S.

Dataset Context and Purpose

As Airbnb continues to impact urban rental markets, this dataset allows analysts, researchers, and real estate professionals to investigate how the short-term rental market shapes the local economy and influences housing availability. Users can leverage this dataset to perform location-based analysis, identify seasonal occupancy trends, and explore the popularity of amenities and property types.

Content

id: Unique identifier assigned to each property listing.

name: Property listing name as provided by the host.

host_id:Unique identifier assigned to the host of the property.

host_name:Name of the host associated with the property.

host_since:Date on which the host joined Airbnb.

host_response_time: Typical response time of the host to guest inquiries.

host_response_rate:Percentage of guest inquiries that the host responded to.

host_is_superhost: Indicates whether the host is a Superhost (True/False).

neighbourhood_cleansed: Neighborhood name where the property is located.

neighbourhood_group_cleansed: Standardized neighborhood group or district where the property is located.

latitude: Geographic latitude coordinate.

longitude: Geographic longitude coordinate.

property_type: Type of property.

room_type: Type of room offered (e.g., Entire home/apt, Private room, Shared room).

accommodates: Maximum number of guests that the property can accommodate.

bathrooms: Number of bathrooms in the property.

bedrooms: Number of bedrooms in the property.

beds: Number of beds in the property.

price: Total price based on minimum nights required for booking.

minimum_nights: Minimum number of nights required for a booking.

availability_365:Number of days the property is available for booking in the next 365 days.

number_of_reviews: Total number of reviews received for the property.

review_scores_rating: Average rating score based on guest reviews (5 is maximum value).

license: License, if applicable.

instant_bookable: Indicates whether guests can book the property instantly (True/False).

Inspiration

• Host Insights: Analyze host behavior, response times, and Superhost status to understand their impact on guest satisfaction and property performance.
• Property Characteristics: Identify popular property types, room types, and amenities, and how they correlate with pricing and occupancy rates.
• Neighborhood Analysis: Explore neighborhood-level trends in pricing, occupancy, and guest reviews to identify popular areas and potential investment opportunities.
• Pricing Strategies: Analyze factors influencing pricing, such as property type, location, amenities, and seasonality.

Source

This dataset is part of Inside Airbnb, Los Angeles California on September 04, 2024. Link found here

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. The platform also allows consumers to book "experiences" in the regions they visit. In 2024, Airbnb reported over *** million booked nights and experiences. How much revenue does Airbnb make? In 2024, the total revenue of Airbnb worldwide increased by nearly ten percent over the previous year. This continued the upward trend which the company has experienced since recovering from the coronavirus (COVID-19) pandemic. ************* generated the highest share of Airbnb’s worldwide revenue in 2024, at **** billion U.S. dollars. How many people visit the Airbnb website? Airbnb ranked ***** among the most popular travel and tourism websites worldwide based on average monthly visits, behind *******************************. In 2024, airbnb.com saw its highest number of unique global visitors in March, at *** million. Meanwhile, Airbnb ranked fourth among leading travel apps globally, with over ** million downloads in 2024.

Airbnb In NYC

Room Prices, Reviews, and Availability

By Huggingface Hub [source]

About this dataset

This dataset offers a unique and comprehensive look into the expansive Airbnb industry in New York City. We capture 20,000+ Airbnbs with its associated data such as descriptions, rates, reviews and availability. Professionals researching this industry will find it an invaluable resource in providing insight to the ever popular Airbnb market that can be used for their advantage.

This dataset showcases some of the most important attributes for each listing: host name, neighborhood group, location (latitude/longitude coordinates), room type, price per night, minimum nights required to book a stay at this listing , total number of reviews and ratings received by guests over time (including reviews per month and last review date), calculated host listing count (indicates how many listings are offered by each host) along with 365 days worth of availability score. With all these parameters one can understand dynamics of demand & supply & further utilize them accordingly to maximize returns or occupancy greeting never before seen transparency into NYC’s Airbnb scene

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How to use the dataset

This dataset can be used to gain a comprehensive understanding of the Airbnb market in New York City. The data offers descriptions, rates, reviews and availability for over 20,000 Airbnbs in NYC.

Here are few tips on how to use this dataset: - Use the latitude and longitude coordinates to visualize the variety of Airbnbs located across all five boroughs of New York City using mapping programs like Google Maps or ArcGIS. - Determine the versatile price ranges offered by Airbnb listings by looking at the “price” column available for each listing . - Analyze reviews scored by guests who have used an Airbnb in order to better understand customer experience with different services through columns such as “number_of_reviews” and “last_review.
4 Understand how often properties are made available for booking based on their popularity through columns like “availability_365 and reviews_per_month. . 5 Investigate listing host data by looking into their description (host name) as well as number of listings they have booked (calculated host listing count)

Research Ideas

• Determining the listings with the highest satisfaction ratings for potential customers to book.
• Analyzing neighborhood trends in prices, availability, and reviews to identify hot areas of competition within the Airbnb market.
• Predicting future prices throughput examining properties such as review scores and availability rate to provide forecast information to AirBnB owners

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group | The neighbourhood group the Airbnb listing is located in. (String) | | latitude | The latitude coordinate of the Airbnb listing. (Float) | | longitude | The longitude coordinate of the Airbnb listing. (Float) | | room_type | The type of room offered by the Airbnb listing. (String) | | price | The price per night of the Airbnb listing. (Integer) | | minimum_nights | The minimum number of nights required for booking the Airbnb listing. (Integer) | | number_of_reviews | T...

The region with the most nights and experiences booked with Airbnb worldwide in 2024 was Europe, the Middle East, and Africa (or EMEA). That year, the EMEA region reported *** million bookings. Asia Pacific had the lowest number of bookings at ** million. The Asia Pacific region also had the lowest average number of nights per Airbnb booking in 2024.

See the average Airbnb revenue & other vacation rental data in Mumbai in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Description: The dataset contains information on property listings from Airbnb, an online marketplace connecting hosts offering accommodations with guests seeking lodging in various locales. Specifically, it includes data on the number of property images associated with each listing and the corresponding number of bookings it attracts. Additionally, the dataset highlights a significant trend that Airbnb has witnessed indicating an intriguing trend that suggests a correlation between the number of property images associated with a listing and the number of bookings it attracts. It also addresses the issue of redundant listings lacking associated images, which fail to attract bookings.

Variables in Listing dataset: Here's a data dictionary for the given dataset:

1. id

   • Description: Unique identifier for each listing.
   • Data Type: String or Integer

2. host_days

   • Description: Number of days since the host joined the platform.
   • Data Type: Integer

3. host_response_time

   • Description: Time taken by the host to respond to inquiries.
   • Data Type: Categorical (e.g., within an hour, within a few hours, within a day, not available)

4. host_response_rate

   • Description: Percentage of inquiries to which the host responds.
   • Data Type: Numeric (Percentage)

5. host_acceptance_rate

   • Description: Percentage of booking requests accepted by the host.
   • Data Type: Numeric (Percentage)

6. host_is_superhost

   • Description: Indicates whether the host is a superhost or not.
   • Data Type: Boolean (True/False)

7. host_listings_count

   • Description: Number of listings managed by the host.
   • Data Type: Integer

8. host_identity_verified

   • Description: Indicates whether the host's identity is verified.
   • Data Type: Boolean (True/False)

9. neighbourhood_cleansed

   • Description: Name of the neighborhood where the listing is located (cleaned version).
   • Data Type: String

10. city

    • Description: Name of the city where the listing is located.
    • Data Type: String

11. property_type

    • Description: Type of property (e.g., apartment, house, villa).
    • Data Type: String

12. room_type

    • Description: Type of room available (e.g., entire home/apt, private room, shared room).
    • Data Type: String

13. accommodates

    • Description: Maximum number of guests accommodated.
    • Data Type: Integer

14. bathrooms

    • Description: Number of bathrooms in the listing.
    • Data Type: Numeric

15. bedrooms

    • Description: Number of bedrooms in the listing.
    • Data Type: Numeric

16. beds

    • Description: Number of beds in the listing.
    • Data Type: Numeric

17. bed_type

    • Description: Type of bed (e.g., real bed, sofa bed, futon).
    • Data Type: String

18. price

    • Description: Price per night for the listing.
    • Data Type: Numeric

19. security_deposit

    • Description: Amount of security deposit required for the booking.
    • Data Type: Numeric

20. cleaning_fee

    • Description: Cleaning fee charged for the booking.
    • Data Type: Numeric

21. guests_included

    • Description: Number of guests included in the base price.
    • Data Type: Integer

22. extra_people

    • Description: Additional fee for extra guests beyond the included number.
    • Data Type: Numeric

23. minimum_nights

    • Description: Minimum number of nights required for booking.
    • Data Type: Integer

24. review_scores_rating

    • Description: Overall rating score for the listing.
    • Data Type: Numeric

25. review_scores_accuracy

    • Description: Rating score for accuracy of listing description.
    • Data Type: Numeric

26. review_scores_cleanliness

    • Description: Rating score for cleanliness of the listing.
    • Data Type: Numeric

27. review_scores_checkin

    • Description: Rating score for check-in process.
    • Data Type: Numeric

28. review_scores_communication

    • Description: Rating score for host communication.
    • Data Type: Numeric

29. review_scores_location

    • Description: Rating score for listing location.
    • Data Type: Numeric

30. review_scores_value

    • Description: Rating score for value provided by the listing.
    • Data Type: Numeric

31. instant_bookable

    • Description: Indicates whether instant booking is available for the listing.
    • Data Type: Boolean (True/False)

32. cancellation_policy

    • Description: Policy governing cancellation of bookings.
    • Data Type: Categorical (e.g., flexible, moderate, strict)

33. reviews_per_month

    • Description: Average number of reviews received per month.
    • Data Type: Numeric

See the average Airbnb revenue & other vacation rental data in New Delhi in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Netherlands Airbnb Listings

Location, Property Types and Reviews

By Amber Ewart [source]

About this dataset

This dataset contains detailed information about the accommodation listings on Airbnb including the characteristics of each listing and the feedback from guests. It includes data on host names, years in service, neighbourhoods, cities and states where the listings are located, zip codes, countries, coordinates(latitude/longitude), property type, room type capacity(accommodates), number of bathrooms and bedrooms/ beds as well as other amenities such as bed types. Furthermore pricing data is also included along with extra people charges, minimum nights per stay and host response time / rate. Additionally number of reviews left by guests is also available along with individual ratings based on accuracy cleanliness check-in communication location value etc.. These metrics provide invaluable insights into properties listed on Airbnb giving potential customers an informed decision platform

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Featured Notebooks

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How to use the dataset

This dataset contains information about Airbnb listings, including the host information, location, property type, room type, amenities offered, price points of the listings and review scores. This data can be used to understand Airbnb trends in various cities and uncover areas with potential for higher demand.

Analyzing this data can help Airbnb hosts determine high-demand areas for their rental properties and maximize bookings by understanding which amenities are attracting more customers and exactly how much people are willing to pay for different types of accommodation.

Research Ideas

• Identifying popular areas to evaluate new listing opportunities in cities with a high demand for Airbnb rentals
• Analyzing competition among existing listings and identifying key factors that could drive success (e.g., price points, amenities, etc.)
• Predicting future user behavior based on reviews and ratings of existing bookings to provide actionable insights for hosts

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Unit_1_Project_Dataset (1).csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------| | host_name | Name of the host of the listing. (String) | | host_since_year | Year the host joined AirBnb. (Integer) | | host_since_anniversary | Anniversary of the host joining AirBnb. (Integer) | | neighbourhood_cleansed | The neighbourhood the listing is located in. (String) | | city | The city the listing is located in. (String) | | state | The state the listing is located in. (String) | | zipcode | The zipcode of the listing. (Integer) | | country | The country the listing is located in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | property_type | The type of property the listing is. (String) | | room_type | The type of room the listing is. (String) | | accommodates | The number of people the listing can accommodate. (Integer) | | bathrooms | The number of bathrooms the listing has. (Integer) | | bedrooms | The number of bedrooms the listing has. (Integer) | | beds | The number of beds the listing has. (Integer) | | bed_type | The type of bed the listing has. (String) | | price | The price of the listing. (Float) | | guests_included | The number of guests included in the price. (Integer) | | extra_people | The additional cost for extra people. (Float) | | minimum_nights | The minimum number of nights a guest must stay. (Integer) | | host_response_time | The time it takes for the host to respond ...

See the average Airbnb revenue & other vacation rental data in Bali in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

See the average Airbnb revenue & other vacation rental data in Athens in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

The Airbnb Accommodation Booking Data Warehouse (2020-2024) is a dataset for business intelligence, and it has a dimensional model comprising four dimension tables and one fact table.

The Dim_Date table provides detailed date information from 2020 to 2024, including day, month, quarter, and weekday details for time-based analysis. The Dim_Host table captures information about property hosts, such as superhost status, total listings, and response times. Dim_Property contains details of accommodations, including location, property type, room type, number of rooms, and pricing. Dim_Customer includes customer demographics such as age group, gender, nationality, and customer segment.

The central Fact_Bookings table records booking transactions, including revenue, nights booked, guests, and fees. Each booking links to specific hosts, customers, properties, and dates through foreign keys.

The dataset supports multi-year analysis of booking trends, revenue performance, customer behaviour, and host activity. It enables insights into seasonal patterns, location performance, and customer segmentation, allowing for strategic decisions in pricing, marketing, and operational planning.

See the average Airbnb revenue & other vacation rental data in Kuala Lumpur in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Unlock the full potential of the short-term rental market with our comprehensive Airbnb Listing Data. This dataset provides a granular, 360-degree view of listing performance, property characteristics, and market dynamics across key global geographies. Designed for Real Estate Investors, Property Managers, Hedge Funds, and Travel Analysts, our data serves as the backbone for data-driven decision-making in the hospitality sector.

Whether you are looking to optimize pricing strategies, identify high-yield investment neighborhoods, or analyze amenity trends, this dataset delivers the raw intelligence required to stay ahead of the competition. We capture high-fidelity signals on listings, availability, pricing, and reviews, allowing you to model supply and demand with precision.

Key Questions This Data Answers Our data is structured to answer the most pressing commercial questions in the short-term rental industry. By leveraging our granular fields, analysts can immediately address:

Market Composition: What is the exact distribution of property types (Entire Home vs. Private Room vs. Shared) in a specific market? Understand supply saturation instantly.

Amenity ROI: Which amenities are most common in top-performing listings? Correlate features (e.g., Pools, Hot Tubs, Wi-Fi speeds) with Occupancy Rates and ADR (Average Daily Rate) to determine the ROI of renovations.

Pricing Intelligence: How does nightly price vary by neighborhood, seasonality, and property type? Visualize price elasticity and identify arbitrage opportunities between sub-markets.

Geospatial Density: What is the density of listings in different geographical areas? Pinpoint "hot zones" for tourism and identify underserved areas ripe for new inventory.

Performance Benchmarking: How do my listings compare to the top 10% of competitors in the same zip code?

Comprehensive Use Cases 1. Market Analysis & Competitive Positioning Gain a competitive edge by understanding the landscape of any target city.

Competitor Mapping: Track the growth of listing supply in real-time. Identify which property managers control the market share.

Saturation Analysis: Avoid over-supplied markets. Use density metrics to find neighborhoods with high demand but low inventory.

Trend Forecasting: Analyze historical data to predict future supply shifts and market saturation points before they occur.

1. Pricing Strategy & Revenue Management Move beyond static pricing. Our data enables dynamic pricing models based on real-world market conditions.

Attribute-Based Pricing: Quantify exactly how much a "Sea View" or "King Bed" adds to the nightly rate.

Seasonality Adjustments: Optimize calendars by analyzing historical price surges during holidays, events, and peak seasons.

RevPAR Optimization: Balance Occupancy and ADR to maximize Revenue Per Available Room (RevPAR).

1. Real Estate Investment & Valuation For investors and funds, this data acts as a fundamental layer for asset valuation.

Cap Rate Calculation: Combine our revenue data with property values to estimate potential yields and Cap Rates for prospective acquisitions.

Investment Scouting: Filter entire regions by "High Occupancy / Low Price" to find undervalued assets.

Due Diligence: Validate seller claims regarding income potential with independent, third-party data history.

1. Property Type & Amenity Distribution Analysis Understand what guests actually want.

Amenity Gap Analysis: Identify amenities that are in high demand (high search volume) but low supply in specific neighborhoods.

Renovation Planning: Data-driven insights on whether installing A/C or allowing pets will significantly increase booking conversion.

Data Dictionary & Key Attributes Our schema is designed for financial modeling and granular analysis. We provide over 50 distinct fields per listing, including calculated financial metrics for Trailing Twelve Months (TTM) and Last 90 Days (L90D).

Listing Identity & Characteristics:

listing_id: Unique identifier for the listing

listing_name & cover_photo_url: Title and main visual

listing_type & room_type: Property classification (e.g., villa, entire home)

amenities: Comprehensive list of offered features

min_nights & cancellation_policy: Booking rules and restrictions

instant_book & professional_management: Operational indicators

Property Specs & Capacity:

guests, bedrooms, beds, baths: Full capacity details

latitude, longitude, city, state, country: Precise geospatial coordinates

photos_count: Quantity of listing images

Host Intelligence:

host_id & host_name: Primary operator details

cohost_ids & cohost_names: Extended management team details

superhost: Quality badge status

Financial Performance (TTM - Trailing 12 Months):

ttm_revenue & ttm_revenue_native: Total gross revenue generated

ttm_avg_rate (ADR): Average Daily Rate achieved

ttm_occupancy & ttm_adjusted_occupancy: Raw vs. Adjusted (excluding owner blocks) occupancy

ttm_revpar & ttm_adjusted_revpar: Revenue Per ...

See the average Airbnb revenue & other vacation rental data in Sydney in 2025 by property type & size, powered by Airbtics. Find top locations for investing.